Phishing Detection in the Gen-AI Era: Quantized LLMs Vs Classical Models

Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency. This paper presents a comparative evaluation of traditional Machine Learning ML, Deep Learning DL, and quantized...

Efficient Unlearning with Privacy Guarantees

Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning ML models trained on them. Machine unlearning has emerged as a practical means to facilitate model forgetting of data...

Adaptive Malware Detection Using Sequential Feature Selection: a Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification

Traditional malware detection methods exhibit computational inefficiency due to exhaustive feature extraction requirements, creating accuracy-efficiency trade-offs that limit real-time deployment. We formulate malware classification as a Markov Decision Process with episodic feature acquisition a...

UniAud: a Unified Auditing Framework for High Auditing Power and Utility with One Training Run

Differentially private DP optimization has been widely adopted as a standard approach to provide rigorous privacy guarantees for training datasets. DP auditing verifies whether a model trained with DP optimization satisfies its claimed privacy level by estimating empirical privacy lower bounds...

Novel Blockchain-Based Protocols for Electronic Voting and Auctions

Programmable blockchains have long been a hot research topic given their tremendous use in decentralized applications. Smart contracts, using blockchains as their underlying technology, inherit the desired properties such as verifiability, immutability, and transparency, which make it a great sui...

ARMOR: Robust Reinforcement Learning-Based Control for UAVs under Physical Attacks

Unmanned Aerial Vehicles UAVs depend on onboard sensors for perception, navigation, and control. However, these sensors are susceptible to physical attacks, such as GPS spoofing, that can corrupt state estimates and lead to unsafe behavior. While reinforcement learning RL offers adaptive control...

[SECURITY] Fedora 41 Update: mirrorlist-server-3.0.7-7.fc41

The mirrorlist-server uses the data created by MirrorManager2 https://github.com/fedora-infra/mirrormanager2 to answer client request for the "best" mirror. This implementation of the mirrorlist-server is written in Rust. The original version of the mirrorlist-server was part of the MirrorManager...

[SECURITY] Fedora 42 Update: mirrorlist-server-3.0.7-7.fc42

The mirrorlist-server uses the data created by MirrorManager2 https://github.com/fedora-infra/mirrormanager2 to answer client request for the "best" mirror. This implementation of the mirrorlist-server is written in Rust. The original version of the mirrorlist-server was part of the MirrorManager...

ZKPROV: a Zero-Knowledge Approach to Dataset Provenance for Large Language Models

As the deployment of large language models LLMs grows in sensitive domains, ensuring the integrity of their computational provenance becomes a critical challenge, particularly in regulated sectors such as healthcare, where strict requirements are applied in dataset usage. We introduce ZKPROV, a...

Communication-Efficient Publication of Sparse Vectors under Differential Privacy

Whitepaper called Communication-Efficient Publication Of Sparse Vectors Under Differential Privacy...

Weseek Growi 安全漏洞

Weseek Growi is an open source wiki system that can be written in Markdown by the Japanese company Weseek. A security vulnerability exists in Weseek Growi versions prior to 7.1.6, which stems from a regular expression efficiency issue that could lead to a denial of service attack...

CVE-2025-6493 CodeMirror Markdown Mode markdown.js redos

A weakness has been identified in CodeMirror up to 5.65.20. Affected is an unknown function of the file mode/markdown/markdown.js of the component Markdown Mode. This manipulation causes inefficient regular expression complexity. It is possible to initiate the attack remotely. The exploit has bee...

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure

The evolution of smart cities demands scalable, secure, and energy-efficient architectures for real-time data processing. With the number of IoT devices expected to exceed 40 billion by 2030, traditional cloud-based systems are increasingly constrained by bandwidth, latency, and energy limitation...

A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis

Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed fo...

Reversing the Paradigm: Building AI-First Systems with Human Guidance

The relationship between humans and artificial intelligence is no longer science fiction -- it's a growing reality reshaping how we live and work. AI has moved beyond research labs into everyday life, powering customer service chats, personalizing travel, aiding doctors in diagnosis, and supporti...

CnC-PRAC: Coalesce, Not Cache, Per Row Activation Counts for an Efficient In-DRAM Rowhammer Mitigation

JEDEC has introduced the Per Row Activation Counting PRAC framework for DDR5 and future DRAMs to enable precise counting of DRAM row activations using per-row activation counts. While recent PRAC implementations enable holistic mitigation of Rowhammer attacks, they impose slowdowns of up to 10% d...

FAME: a Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes

The widespread emergence of face-swap Deepfake videos poses growing risks to digital security, privacy, and media integrity, necessitating effective forensic tools for identifying the source of such manipulations. Although most prior research has focused primarily on binary Deepfake detection, th...

Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems

The growing deployment of deep learning models in real-world environments has intensified the need for efficient inference under strict latency and resource constraints. To meet these demands, dynamic deep learning systems DDLSs have emerged, offering input-adaptive computation to optimize runtim...

Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging

The rapid advancements in autonomous vehicle AV technology promise enhanced safety and operational efficiency. However, frequent lane changes and merging maneuvers continue to pose significant safety risks and disrupt traffic flow. This paper introduces the Minimizing Lane Change Algorithm MLCA, ...

ReDASH: Fast and efficient Scaling in Arithmetic Garbled Circuits for Secure Outsourced Inference

Whitepaper called ReDASH: Fast and efficient Scaling in Arithmetic Garbled Circuits for Secure Outsourced Inference...